ReIn: Conversational Error Recovery with Reasoning Inception

TL;DR

The paper addresses errors arising in open-ended, tool-enabled dialogue systems by user actions and proposes Reasoning Inception (ReIn), a test-time intervention that injects an initial reasoning block into a fixed, pre-trained agent to steer error recovery without changing parameters or prompts. An external inception module detects predefined error types and supplies recovery plans that are embedded into the agent’s internal reasoning, enabling corrective actions during dialogue. Empirical results on curated airline and retail scenarios show substantial gains in task success, generalization to unseen errors, and favorable comparisons to prompt-modification baselines, with additional insights into dynamic activation and instruction hierarchy. The work highlights practical benefits for deploying robust, on-the-fly error recovery in production systems and suggests safe integration of recovery tools with fixed models, potentially guiding future self-monitoring conversational agents.

Abstract

Conversational agents powered by large language models (LLMs) with tool integration achieve strong performance on fixed task-oriented dialogue datasets but remain vulnerable to unanticipated, user-induced errors. Rather than focusing on error prevention, this work focuses on error recovery, which necessitates the accurate diagnosis of erroneous dialogue contexts and execution of proper recovery plans. Under realistic constraints precluding model fine-tuning or prompt modification due to significant cost and time requirements, we explore whether agents can recover from contextually flawed interactions and how their behavior can be adapted without altering model parameters and prompts. To this end, we propose Reasoning Inception (ReIn), a test-time intervention method that plants an initial reasoning into the agent's decision-making process. Specifically, an external inception module identifies predefined errors within the dialogue context and generates recovery plans, which are subsequently integrated into the agent's internal reasoning process to guide corrective actions, without modifying its parameters or system prompts. We evaluate ReIn by systematically simulating conversational failure scenarios that directly hinder successful completion of user goals: user's ambiguous and unsupported requests. Across diverse combinations of agent models and inception modules, ReIn substantially improves task success and generalizes to unseen error types. Moreover, it consistently outperforms explicit prompt-modification approaches, underscoring its utility as an efficient, on-the-fly method. In-depth analysis of its operational mechanism, particularly in relation to instruction hierarchy, indicates that jointly defining recovery tools with ReIn can serve as a safe and effective strategy for improving the resilience of conversational agents without modifying the backbone models or system prompts.

Figures (13)

• Figure 1: The overview of framework. An inception module detects potentially erroneous user queries and generates a reasoning block with proper recovery plans (Inception Block). A task agent with fixed parameters and system prompts dynamically adjusts its behavior (blue) by receiving the initial reasoning block (green) from the inception module. \ref{['alg:reasoning_inception']} demonstrates the formal procedure in turn $t$, and \ref{['sec:example_inception']} illustrates examples of inception blocks containing recovery plans.
• Figure 2: The average Pass@1 (with standard error of the mean) of task agents employing different inception modules across seen scenarios (i.e., Anaphora, Multiple Interpretation, Action, and Parameter) in the retail domain. See \ref{['sec:performance_per_situation_retail']} for decomposed results and \ref{['sec:performance_airline_seen']} for airline domain results.
• Figure 3: The average Pass@1 of task agents employing different inception modules across seen (i.e., Anaphora, Multiple Interpretation, Action, and Parameter) and unseen (i.e., Contradiction and Domain) scenarios in the retail domain. See \ref{['fig:airline_unseen']} for airline domain results.
• Figure 4: Comparison between prompt-preserving and prompt-modifying methods in the retail domain. See \ref{['sec:modification_full']} for all domain results.
• Figure 5: Comparison between controlled vs. dynamic application in the airline domain. INT (Multiple Interpretation) and ANA (Anaphora) are ambiguous, while ACT (Action) and PAR (Parameter) are unsupported scenarios.